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Dynamical stochastic resonance for nonuniform illumination image enhancementhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5634
2018-08-10T00:00:00ZDeep Learning Features for Robust Facial Kinship verificationhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5552
2018-08-10T00:00:00ZPower-Law Transform based Spectral Features for Texture Image Retrievalhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5604
2018-08-09T00:00:00ZStatistical Characterization of Block Variance and AC DCT Coefficients for Power Law Enhanced Imageshttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5596
2018-08-09T00:00:00ZView Synthesis Method for 3D Video Coding Based on Temporal and Inter View Correlationhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5390
2018-08-06T00:00:00ZAn Efficient Interpolated Compressed Sensing Reconstruction Scheme for 3D MRIhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5473
2018-08-06T00:00:00ZVolume 12, Issue 8http://digital-library.theiet.org/content/journals/iet-ipr/12/8
2018-08-01T00:00:00ZLocal multiscale blur estimation based on toggle mapping for sharp region extractionhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0095
2018-07-27T00:00:00ZMSDNN: Multi-Scale Deep Neural Network for Salient Object Detectionhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5631
2018-07-26T00:00:00ZDetection and Analysis of Large-Scale Wind Turbine Blade Surface CracksBased on UAV-Taken Imageshttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5542
2018-07-26T00:00:00ZChange Detection in Landsat Images based on Local Neighbourhood Informationhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5524
2018-07-26T00:00:00ZEnhancement of dim targets in a sea background based on long-wave infrared polarization featureshttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5607
2018-07-26T00:00:00ZWatermarking image encryption using deterministic phase mask and singular value decomposition in fractional Mellin transform domainhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5399
2018-07-20T00:00:00ZUsing a GMM approach and 2D-GARCH modeling for denoising ultrasound imageshttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5528
2018-07-20T00:00:00ZTwo Improved Extension of Local Binary Pattern Descriptors using Wavelet Transform for Texture Classificationhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5410
2018-07-20T00:00:00ZBlind image quality assessment based on Benford's lawhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5385
2018-07-20T00:00:00ZA Two-Step Evidential Fusion Approach for Accurate Breast Region Segmentation in Mammogramshttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5325
2018-07-20T00:00:00ZA Fully Automated Brain Tumor Segmentation System in 3D-MRI using Symmetry Analysis of Brain and Level-Setshttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5332
2018-07-20T00:00:00ZBAT algorithm inspired retinal blood vessel segmentationhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1266
<p>The automated extraction of retinal blood vessels is the course of action in the medical analysis of retinal diseases. The proposed methodology for the retinal vessel segmentation is based on BAT algorithm and random forest classifier. A feature vector of 40-dimensional including local, phase and morphological features is extracted and the feature set which minimises the classifier error is identified by BAT algorithm. The selected features are also identified as the dominant features in the classification. Performance of the proposed method is analysed by the publicly available databases such as digital retinal images for vessel extraction and structured analysis of the retina. The authors’ proposed method is highly sensitive to identify the blood vessels, in view of the fact that it corresponds to the ability of the method to identify the blood vessels correctly. BAT algorithm-based proposed method achieves very high sensitivity and accuracy of about 82.85 and 95.34%, respectively.</p>2018-07-19T00:00:00ZOcclusion-robust object tracking based on the confidence of online selected hierarchical featureshttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5454
2018-07-17T00:00:00ZImage segmentation algorithm based on superpixel clusteringhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5439
<p>The main task of image segmentation is to partition an image into disjoint sets of pixels called clusters. Spectral clustering algorithm has been developed rapidly in recent years and it has been widely used in image segmentation. The traditional spectral clustering algorithm requires huge amount of computation to process colour images with high resolution. While one possible solution is reducing image resolution, but it will lead to the loss of image information and reduce segmentation performance. To overcome the problem of traditional spectral clustering, an image segmentation algorithm based on superpixel clustering is proposed. Firstly, the algorithm uses the superpixel preprocessing technique to quickly divide the image into a certain number of superpixel regions with specific information. Then, the similarity matrix is used to provide the input information to the spectral clustering algorithm to cluster the superpixel regions and get the final image segmentation results. The experiment results show that the proposed algorithm can effectively improve the performance in image segmentation compared with the traditional spectral clustering algorithm, and finally the substantial improvement has been obtained in respect of computational complexity, processing time and the overall segmentation effect.</p>2018-07-17T00:00:00ZVolume 12, Issue 7http://digital-library.theiet.org/content/journals/iet-ipr/12/7
2018-07-01T00:00:00ZUsing feature points and angles between them to recognise facial expression by a neural network approachhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.0009
<p>In this study, the authors propose a neural network (NN) method that uses feature points and the angles formed between the points to recognise facial expressions. Accurate facial expression recognition is an important part of affective computing with many practical applications. Yet, achieving acceptable levels of facial recognition accuracy has proven difficult. Feature points and the distances between the points are used to model basic expressions in NN-based approaches, but, in some cases, they cannot generate satisfactory performance. They expand on the characterisation of facial expression by considering the angles formed between feature points to augment the amount of information that is sent to the NNs. Furthermore, to circumvent a common challenge in facial expressions recognition, which is the difficulty of differentiating among several expressions, they designed a post-processing step to assess the output of the NN against a threshold. The whole method makes a decision only when the output of the NN exceeds the threshold. Otherwise, the frame under consideration is assigned to a ‘no decision’ class. They tested our method on the widely used facial expression CK + database and found that it can achieve good accuracy.</p>2018-06-22T00:00:00ZSemantic image segmentation using an improved hierarchical graphical modelhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0738
<p>Hierarchical graphical models can incorporate jointly several tasks in a unified framework. By applying this approach, information exchange among tasks would improve the results. A hierarchical conditional random field (CRF) is proposed here to improve the semantic image segmentation. Although this newly proposed model applies the information of several tasks, its run time is comparable with the contemporary approaches. This method is evaluated on MSRC dataset and has shown similar or better segmentation accuracy in comparison with models where CRFs or hierarchical models are adopted.</p>2018-06-22T00:00:00ZHandwritten Hindi character recognition: a reviewhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0184
<p>As the years passed by, computers became more powerful and automation became the need of generation. Humans tried to automate their work and replace themselves with machines. This effort of transition from manual to automatic gave rise to various research fields, and document character recognition is one such field. From the last few years, there is a sincere contribution from researchers for the development of optical character recognition systems for various scripts and languages. As a result of intensive research and development, there has been a significant improvement in handwritten devnagari text recognition. The main focus of this study is detailed survey of existing techniques for recognition of offline handwritten Hindi characters. It addresses all the aspects of Hindi character recognition starting from database to various phases of character recognition. The most relevant techniques of preprocessing, feature extraction and classification are discussed in various sections of this study. Moreover, this study is a zest of work accepted and published by research community in recent years. This study benefits its readers by discussing limitations of existing techniques and by providing beneficial directions of research in this field.</p>2018-06-22T00:00:00ZGradation of diabetic retinopathy on reconstructed image using compressed sensinghttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1013
<p>This study explores neovascularisation and lesion detection in an integrated framework for gradation in diabetic retinopathy (DR). Imaging is assumed to be done from sub-sample measurements following compressed sensing. Blind estimation of the scale of the matched filter (MF) followed by fuzzy entropy maximisation is done for extraction and classification of the thick and the thin vessels. Mutual information (MI) between vessel density and tortuosity of the thin vessel class is maximised in two dimensions (2D) for neovascularisation detection. For lesion detection, MI between the maximum MF response and the maximum Laplacian of Gaussian filter response is jointly maximised in 2D. The outcomes are then combined in a common platform for gradation in DR. Simulation results demonstrate that 95% images of each of DRIVE, STARE and DIARETDB1 databases and 94% images of MESSIDOR database are correctly graded by the proposed method when 80% measurement space is considered.</p>2018-06-22T00:00:00ZFully automated brain tumour segmentation system in 3D-MRI using symmetry analysis of brain and level setshttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1124
<p>This study presents a new fully automated, fast, and accurate brain tumour segmentation method which automatically detects and extracts whole tumours from 3D-MRI. The proposed method is based on a hybrid approach that relies on a brain symmetry analysis method and a combining region-based and boundary-based segmentation methods. The segmentation process consists of three main stages. In the first one, image pre-processing is applied to remove any noise, and to extract the brain from the head image. In the second stage, automated tumour detection is performed. It is based essentially on FBB method using brain symmetry. The obtained result constitutes the automatic initialisation of a deformable model, thus removing the need of selecting the initial region of interest by the user. Finally, the third stage focuses on the application of region growing combined with 3D deformable model based on geodesic level-set to detect the tumour boundaries containing the initial region, computed previously, regardless of its shape and size. The proposed segmentation system has been tested and evaluated on 3D-MRIs of 285 subjects with different tumour types and shapes obtained from BraTS'2017 dataset. The obtained results turn out to be promising and objective as well as close to ground truth data.</p>2018-06-22T00:00:00ZDeep learning-based approach to latent overlapped fingerprints mask segmentationhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1227
<p>Overlapped fingerprints can be potentially present in several civil applications and criminal investigations. Segmentation of overlapped fingerprints is a required step in the process of fingerprint separation and subsequent verification. Overlapped fingerprint segmentation is performed manually (and the resulting manually drawn masks are a required additional input) in all of the overlapped latent fingerprints separation approaches in the literature, which make them only semi-automatic. This study proposes a novel overlapped fingerprint mask segmentation approach, thereby filling that gap in the development of fully automated fingerprint separation solutions. The proposed method uses convolutional neural networks to classify image blocks into three classes – background, single region, and overlapped region. The proposed approach shows satisfactory performance on three different datasets and opens the door for full automation of fingerprint separation algorithms, which is a very promising research area.</p>2018-06-06T00:00:00ZVolume 12, Issue 6http://digital-library.theiet.org/content/journals/iet-ipr/12/6
2018-06-01T00:00:00ZMotion and illumination defiant cut detection based on Weber featureshttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1237
<p>The spontaneous proliferation of video data necessitates implementing hierarchical structures for various content management applications. Temporal video segmentation is the key towards such management. To address the problem of temporal segmentation, the current communication exploits the concept of psychological behaviour of the human visual system. Towards this goal an abrupt cut detection scheme has been proposed based on Weber's law which provides a strong spatial correlation among the neighbouring pixels. Thus, the authors provide a robust and unique solution for abrupt shot boundary detection when the frames are affected partially or fully by flashlight, fire and flicker, high motion associated with an object or camera. Further, they have devised a model for generating an automatic threshold, taking into account the statistics of the feature vector which quadrates itself with the variation in the contents of the video. The effectiveness of the proposed framework is validated by exhaustive comparison with few contemporary and recent approaches by using benchmark datasets TRECVID 2001, TRECVID 2002, TRECVID 2007 and some publicly available videos. The results obtained give credence to the remarkable improvement in the performance while preserving a good trade-off between missed hits and false hits as compared to the state-of-the-art methods.</p>2018-05-30T00:00:00ZWeighted Kernel joint sparse representation for hyperspectral image classificationhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.0124
<p>Kernel joint sparse representation (KJSR) performs joint sparse representation in the feature space and has shown good performance for the hyperspectral image (HSI) classification. In order to distinguish spatial neighbouring pixels in the feature space, we propose two weighted KJSR (WKJSR) methods in this paper. The first one computes the weight directly based on the kernel similarity between neighbouring pixels. The second weighted scheme uses a nearest regularisation strategy to simultaneously optimise the weights of projected neighbouring pixels and joint sparse representation coefficients. The proposed WKJSR methods can exploit the similarities and differences among neighbouring pixels to obtain accurate weights for the joint sparse representation and classification. Experimental results on two benchmark HSI data sets demonstrate the effectiveness of the proposed methods.</p>2018-05-24T00:00:00ZSegmentation of the lumen and media-adventitial borders in intravascular ultrasound images using a geometric deformable modelhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1143
<p>This study presents a geometric deformable model-based segmentation approach to segmentation of the intima and media-adventitial (MA) borders in sequential intravascular ultrasound (IVUS) images. The initial estimation of the vessel borders was done manually only for the first frame of each sequence. After the border initialisation, pre-processing including edge preservation, noise reduction, and dead zone preservation was successively performed on each IVUS frame. To improve segmentation performance, the image masks were determined preliminarily by local binary pattern-based mask initialisation. Then, the inner and outer borders were approximated using a modified distance regularised level set evolution model. The results showed superior performance of the suggested approach for estimating intima and MA layers from the IVUS images. The corresponding correlation coefficients of area, vessel perimeter, maximum vessel diameter, and maximum lumen diameter were <i>r</i> = 0.782, <i>r</i> = 0.716, <i>r</i> = 0.956, and <i>r</i> = 0.874 for the 20 MHz images, respectively, and <i>r</i> = 0.990, <i>r</i> = 0.995, <i>r</i> = 0.989, and <i>r</i> = 0.996 for the 45 MHz images, respectively. In addition, linear regression analysis indicated that the manual segmentation had significantly high similarity at <i>r</i> > 0.967 and <i>r</i> > 0.993 for 20 and 45 MHz images, respectively.</p>2018-05-24T00:00:00ZNon-subsampled shearlet transform based MRI and PET brain image fusion using simplified pulse coupled neural network and weight local features in YIQ colour spacehttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1298
<p>Magnetic resonance imaging (MRI) and positron emission tomography (PET) image fusion is a recent hybrid modality used in several oncology applications. The MRI image shows the brain tissue anatomy and does not contain any functional information, while the PET image indicates the brain function and has a low spatial resolution. A perfect MRI–PET fusion method preserves the functional information of the PET image and adds spatial characteristics of the MRI image with the less possible spatial distortion. In this context, the authors propose an efficient MRI–PET image fusion approach based on non-subsampled shearlet transform (NSST) and simplified pulse-coupled neural network model (S-PCNN). First, the PET image is transformed to YIQ independent components. Then, the source registered MRI image and the <i>Y</i>-component of PET image are decomposed into low-frequency (LF) and high-frequency (HF) subbands using NSST. LF coefficients are fused using weight region standard deviation (SD) and local energy, while HF coefficients are combined based on S-PCCN which is motivated by an adaptive-linking strength coefficient. Finally, inverse NSST and inverse YIQ are applied to get the fused image. Experimental results demonstrate that the proposed method has a better performance than other current approaches in terms of fusion mutual information, entropy, SD, fusion quality, and spatial frequency.</p>2018-05-24T00:00:00ZMulti-stage filtering for single rainy image enhancementhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1022
<p>Rain image enhancement is important for outdoor computer vision applications. In this study, the authors propose a multi-stage filtering method for single rainy image enhancement. It is based on their new rainy image model, and consists of two main operations: rain streaks removal and rain fog removal. For rain streaks removal, based on one key observation that the low-pass version of a rainy image and that of a non-rainy image of the same scene are almost the same after appropriate low-pass filtering, they remove rain streaks from rainy images by decomposing an input rainy image (or a rainy component image) into the low-frequency (LF) part and the high-frequency (HF) part via an LF smooth filter, i.e. the traditional Gaussian filter with a simple subtraction operation in multiple different stages. After rain streaks removal, dark channel prior-based method was employed for rain fog removal. Experimental results show that the proposed algorithm generated comparable outputs with most of the state-of-the-art algorithms with low computation cost.</p>2018-05-24T00:00:00ZFPR using machine learning with multi-feature methodhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1406
<p>Biometrics authentication is considered as most secure and reliable method to recognise and identify person's identity. Researchers put efforts to find efficient ways to secure and classify the solutions to biometric problems. In this category, fingerprint recognition (FPR) is most widely used biometric trait for person identification/verification. The present work focuses an FPR technique, which uses the grey-level difference method, discrete wavelet transforms and edge histogram descriptor for fingerprint representation and matching. Wavelet shrinkage used for noise removal in the image. Ridge flow estimation is calculated using the gradient approach. SVM and Hamming distance similarity measures are used for recognition. The experiment result has been tested on the standard 2000–2004 fingerprint verification competition dataset and the accuracy of proposed algorithm was reported to be well above 98%.</p>2018-05-24T00:00:00ZColour image encryption via fractional chaotic state estimationhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0817
<p>This study introduces an encryption algorithm for colour RGB images and text, the encryption is based on the synchronisation of fractional chaotic systems, the synchronisation has the topology of master–slave, where the transmitter is the master system and the receiver is the slave system, this last one is designed as a new smoothed sliding modes state observer for fractional chaotic systems. The encryption algorithm provides security against common cryptographic techniques, including known and chosen plaintext attacks.</p>2018-05-24T00:00:00ZAdaptive patched L0 gradient minimisation model applied on image smoothinghttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1223
<p>L0 gradient minimisation model, one of edge-aware image smoothing method, also suffers fromthe stair-casing effect and images with strong textures cannot be smoothed effectively and weak edges or structureswill be smoothed overly. The authors propose a method to overcome these drawbacks above. To begin with, theimage is subjected to non-subsampled shearlet transform to obtain high-frequency component, and combine allhigh-frequency component by maximum local energy rules to obtain the high-frequency decomposition image,afterwards, introducing the data term associated with high-frequency decomposition image to keep the similarityof edge and structure between the input and smoothed image. Secondly, the patched L0 gradient minimisationmodel is presented for improving the description of local information, since different size of the patches has thedifferent texture, exploiting the coefficient of variation to define the size of patch. Finally, defining the adaptivesmoothing coefficient based on the gradient to make sure that the smoothing effect of the patch is optimal. Theproposed model is applied to image smoothing with desirable results successfully, and the comparisons with otherstate-of-the-art edge-preserving image smoothing algorithms demonstrate the great performances ofedge-preserving and texture smoothing.</p>2018-05-24T00:00:00ZProbabilistic binary similarity distance for quick binary image matchinghttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1333
<p>Here, the author presents the gamma binary distance, an exceptional distance for measuring similarity between binary images. The gamma distance is a probabilistic pixel mapping measure that is a modification of the Hamming distance. Employing a probabilistic approach to image matching enables gamma to measure similarity more accurately than employing traditional binary distances. The author shows the advantage of employing the gamma distance for similarity measurement by comparing it to three of the most popular similarity distances used for binary image matching: correlation, sum of the absolute difference method, and mutual information. Results of extensive testing conducted on a large database are presented where the superiority of the gamma distance over other similarity distances is shown.</p>2018-05-08T00:00:00ZVolume 12, Issue 5http://digital-library.theiet.org/content/journals/iet-ipr/12/5
2018-05-01T00:00:00ZHyperspectral image super-resolution under misaligned hybrid camera systemhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1340
<p>Hyperspectral imaging has been widely used for agriculture, astronomy, surveillance, and so on. However, hyperspectral imaging usually suffers from low-spatial resolution, due to the limited photons in individual bands. Recently, more hyperspectral image super-resolution methods have been developed by fusing the low-resolution hyperspectral image and high-resolution RGB image, but most of them did not consider the misalignment between two input images. In this study, the authors present an effective method to restore a high-resolution hyperspectral image from the misaligned low-resolution hyperspectral image and high-resolution RGB image, which exploits spectral and spatial correlation in hyperspectral and RGB images. Specifically, they employ the spectral sparsity to restore the high-resolution hyperspectral image on the misaligned part, and then simultaneously employ spectral and spatial structure correlation to restore the high-resolution hyperspectral image on the aligned area, which can be fused to obtain the high-quality hyperspectral image restoration under a misaligned hybrid camera system. Experimental results show that the proposed method outperforms the state-of-the-art hyperspectral image super-resolution methods under a misaligned hybrid camera system in terms of both objective metric and subjective visual quality.</p>2018-04-30T00:00:00ZAdaptive spatio-temporal background subtraction using improved Wronskian change detection scheme in Gaussian mixture model frameworkhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0595
2018-04-30T00:00:00ZImage splicing detection based on Markov features in discrete octonion cosine transform domainhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1131
<p>To improve the poor robustness and low accuracy of the existing algorithms of image splicing detection, a novel passive image forgery detection method is proposed in this study, which is based on DOCT (discrete octonion cosine transform) and Markov. By introducing the octonion and DOCT, the colour information of six image channels (the RGB model and the HSI model) can be exhaustively extracted, which enhances the robustness of the algorithm. On the issue of improving the detection accuracy, the standard deviation is used to characterise the relationship of the colour information between the parts of DOCT coefficient matrix, and the <i>K</i>-fold cross-validation is introduced to improve the identification performance of the classifier. The steps of the algorithm are as follows: Firstly, the 8 × 8 block DOCT transform is used to the original image to obtain parts of block DOCT coefficient. Secondly, the standard deviation is used to process the corresponding parts of all blocks of the image. Finally, the Markov feature vector of the DOCT coefficient is extracted and feds to the LIBSVM (a library for support vector machines). When using LIBSVM for classification, <i>K</i>-fold cross-validation is executed to select the best parameter pairs. The experiment results demonstrate that the algorithm is superior to the other state-of-the-art splicing detection methods.</p>2018-04-27T00:00:00ZSubjectively correlated estimation of noise due to blurriness distortion based on auto-regressive model using the Yule–Walker equationshttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0916
<p>In this study, a block-based estimation of noise due to blurriness distortion is proposed based on auto-regressive (AR) modelling. In the proposed method; a de-correlated, low-energy version of the blurred image is auto regressively modelled. To this end, AR parameters are estimated using the Yule–Walker equations. As these equations include auto-correlation function (ACF) coefficients, ACF estimation is also required. The Yule–Walker equations are solved making use of Durbin–Levinson algorithm. Finally, noise energy is mathematically defined and computed for each block. Since blurriness is a signal-dependent image distortion, estimating and describing its characteristics via a noise like that of the AR model input, is significant. In fact, extracting features of such ‘noise’ can lead to the design and development of a new method of image quality metrics. Inspired by the ‘stem cells’ concept in medical science that is convertible to other cell types, the AR model input is called ‘stem noise’. To visualise contribution of the ‘Stem Noise’ in the reconstruction of blurriness image distortion, a map called stem noise energy map is created. It is shown that the characteristics of the estimated noise energy are well correlated with the human subjective scores.</p>2018-04-26T00:00:00ZOrthogonal gradient measurement matrix optimisation methodhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0888
<p>The optimisation of measurement matrix that is within the compressive sensing framework is considered in this study. Based on the fact that an information factor with smaller mutual coherence performs better, the gradient measurement matrix optimisation method is improved by an orthogonal search direction revision factor. This algorithm updates the approximation of ideal Gram matrix of information operator and the measurement matrix alternatingly. Using measurement matrix and sparse basis to represent the Gram matrix, the measurement matrix is optimised by the gradient algorithm, in which an orthogonal gradient search direction revision factor is proposed and utilised to further improve the performance of measurement matrix. This orthogonal factor is computed by the Cayley transform of a real skew symmetric matrix that is related to the gradient and the measurement matrix. Results of several experiments show that compared with the initial random matrix, the optimised measurement matrix can lead to better signal reconstruction quality.</p>2018-04-26T00:00:00ZHand gesture recognition using DWT and F-ratio based feature descriptorhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1312
<p>This study demonstrates the development of vision based static hand gesture recognition system using web camera in real-time applications. The vision based static hand gesture recognition system is developed using the following steps: preprocessing, feature extraction and classification. The preprocessing stage consists of illumination compensation, segmentation, filtering, hand region detection and image resize. This study proposes a discrete wavelet transform (DWT) and Fisher ratio (<i>F</i>-ratio) based feature extraction technique to classify the hand gestures in an uncontrolled environment. This method is not only robust towards distortion and gesture vocabulary, but also invariant to translation and rotation of hand gestures. A linear support vector machine is used as a classifier to recognise the hand gestures. The performance of the proposed method is evaluated on two standard public datasets and one indigenously developed complex background dataset for recognition of hand gestures. All above three datasets are developed based on American Sign Language (ASL) hand alphabets. The experimental result is evaluated in terms of mean accuracy. Two possible real-time applications are conducted, one is for interpretation of ASL sign alphabets and another is for image browsing.</p>2018-04-26T00:00:00ZFast matching pursuit for sparse representation-based face recognitionhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1263
<p>Even though face recognition is one of the most studied pattern recognition problems, most existing solutions still lack efficiency and high speed. Here, the authors present a new framework for face recognition which is efficient, fast, and robust against variations of illumination, expression, and pose. For feature extraction, the authors propose extracting Gabor features in order to be resilient to variations in illumination, facial expressions, and pose. In contrast to the related literature, the authors then apply supervised locality-preserving projections (SLPP) with heat kernel weights. The authors’ feature extraction approach achieves a higher recognition rate compared to both traditional unsupervised LPP and SLPP with constant weights. For classification, the authors use the recently proposed sparse representation-based classification (SRC). However, instead of performing SRC using the computationally expensive <script type="math/mml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> </script> minimisation, the authors propose performing SRC using fast matching pursuit, which considerably improves the classification performance. The authors’ proposed framework achieves ∼99% recognition rate using four benchmark face databases, significantly faster than related frameworks.</p>2018-04-26T00:00:00ZCircular trace transform and its PCA-based fusion features for image representationhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1146
<p>To improve the image representation efficiency of trace transform (TT) features to images with circular and arc-shaped textures, the authors propose circular TT (CTT) to extract features. CTT consists of tracing an image with circles around which certain functionals of the image function are calculated. Quadruple CTT features can be generated through three successive functionals in the results of CTT, while different quadruple features can be obtained by choosing different combinations of successive functionals. These quadruple features can represent different texture properties and deeper intrinsic information of an image. By fusing CTT features and TT features based on PCA (FFCT_PCA), they construct a new complementary descriptor with much less dimension, further improving the representation performance for mixed texture images. Experimental results demonstrate that CTT has better performance than TT in recognising images with circular and arc-shaped textures, and FFCT_PCA has the potential to outperform the state-of-the-art feature extraction methods.</p>2018-04-26T00:00:00ZNon-rigid point set registration by high-dimensional representationhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1363
<p>Non-rigid point set registration is a key component in many computer vision and pattern recognition tasks. In this study, the authors propose a robust non-rigid point set registration method based on high-dimensional representation. Their central idea is to map the point sets into a high-dimensional space by integrating the relative structure information into the coordinates of points. On the one hand, the point set registration is formulated as the estimation of a mixture of densities in high-dimensional space. On the other hand, the relative distances are used to compute the local features which assign the membership probabilities of the mixture model. The proposed model captures discriminative relative information and enables to preserve both global and local structures of the point set during matching. Extensive experiments on both synthesised and real data demonstrate that the proposed method outperforms the state-of-the-art methods under various types of distortions, especially for the deformation and rotation degradations.</p>2018-04-25T00:00:00ZMemory-efficient architecture of circle Hough transform and its FPGA implementation for iris localisationhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1167
<p>This study presents a circle Hough transform (CHT) architecture that provides memory reduction between 74 and 93% without and with little degradation in the accuracy, respectively. For an image of <i>P</i> × <i>Q</i> pixels, the standard (direct) CHT requires a two-dimensional (2D) accumulator array of <i>P</i> × <i>Q</i> cells, but the proposed CHT uses a 2D accumulator array of (<i>P/m</i>) × (<i>Q/n</i>) cells for coarse circle detection and two 1D accumulator arrays of <i>P</i> × 1 and <i>Q</i> × 1 cells for fine detection, therein reducing the memory by a factor of <i>m</i> × <i>n</i> (approximately). The proposed CHT architecture was applied to iris localisation application and carried out its comprehensive evaluation. The average accuracy of the proposed CHT for iris localisation (inner plus outer iris-circle detection) is 98% with memory reduction of 87% compared with the direct CHT. The proposed CHT architecture was implemented on field programmable logic array targeting Xilinx Zynq device. The proposed CHT hardware takes processing time of 6.25 ms (average) for iris localisation in an image of 320 × 240 px<sup>2</sup>. The proposed work is compared with the previous work, which shows improved results. Finally, the effect of additive Gaussian noise on the CHT performance is investigated.</p>2018-04-25T00:00:00ZFast enhancement algorithm of highway tunnel image based on constraint of imaging modelhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0902
<p>Due to uneven illumination and dim environment in the tunnel, the monitored image is blurred, which makes it difficult to recognise the traffic status. Therefore, it is necessary to enhance the tunnel image in advance. In this study, a fast image enhancement algorithm based on imaging model constraint is proposed. First, the method uses the combination of global atmospheric light and partitioned atmospheric light to estimate the local atmospheric light. Second, the transmission is estimated based on the formula derived from the imaging model constraints. Third, the method uses a constant instead of illumination to balance tunnel image illumination. Last, the tunnel image is enhanced according to the imaging model. Experimental and comparative analysis results show that the proposed method can rapidly and effectively enhance the tunnel image.</p>2018-04-25T00:00:00ZEntropy-based variational Bayes learning framework for data clusteringhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.0043
<p>A novel framework is developed for the modelling and clustering of proportional data (i.e. normalised histograms) based on the Beta-Liouville mixture model. This framework is based on incremental model selection, by testing if a given component was truly Beta-Liouville distributed. Specifically, the authors compare the theoretical maximum entropy of the given component with the estimated entropy obtained by the MeanNN estimator. If a significant difference was gained from this comparison, this component is considered as not well fitted and is then splitted into two new components with a proper initialisation. Our approach is tested through synthetic data sets and real-world applications which involve human gesture recognition and vehicle tracking for traffic monitoring purposes, which demonstrate that the authors' approach is superior to comparable techniques.</p>2018-04-25T00:00:00ZDenoising hyperspectral images using Hilbert vibration decomposition with cluster validationhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1234
<p>Denoising of hyperspectral images is an essential step to remove the visual artifacts and improve the quality of an image. There are various sources of noise such as dark current, thermal and read noise produced due to detectors, stochastic error of photo-counting and so on which leads to variability of noise both in spatial and spectral domains. In this study, author proposes a novel denoising method based on concept of Hilbert vibration decomposition (HVD). Being iterative in nature it segregates initial amplitude composition into various components which are composed of slow varying wavelength. Any hyperspectral image is captured by the sensor over contiguous wavelengths. Thus, variation in intensities over the spectral dimension is less. HVD separates pixels in decreasing order of their intensity and results in denoising of the image. To evaluate method, various noise conditions have been tested on three real datasets: Washington DC mall, Urban and Pavia University. The validation is done both visually and quantitatively. The denoising with almost 100% mean structural similarity index confirms superiority of the designed method. Clustering and spectral analysis of various denoised images have also been reported. Clustering accuracy of 65% is achieved by the HVD as compared to other methods.</p>2018-04-25T00:00:00ZThree-dimensional image registration using distributed parallel computinghttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1021
<p>Three-dimensional (3D) images have become increasingly popular in practice. They are commonly used in medical imaging applications. In such applications, it is often critical to compare two 3D images, or monitor a sequence of 3D images. To make the image comparison or image monitoring valid, the related 3D images should be geometrically aligned first, which is called image registration (IR). However, IR for 3D images would take much computing time, especially when a flexible method is considered, which does not impose any parametric form on the underlying geometric transformation. Here, the authors explore a fast-computing environment for 3D IR based on the distributed parallel computing. The selected 3D IR method is based on the Taylor's expansion and 3D local kernel smoothing. It is flexible, but involves much computation. The authors demonstrate that this fast-computing environment can effectively handle the computing problem while keeping the good properties of the 3D IR method. The method discussed here is therefore useful for applications involving big data.</p>2018-04-24T00:00:00ZEfficient approach for non-ideal iris segmentation using improved particle swarm optimisation-based multilevel thresholding and geodesic active contourshttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.0917
<p>Segmentation is an important step in iris recognition framework because the accuracy of the iris recognition system is affected by the segmentation of the iris. The image acquisition introduces noise artefacts such as specular reflections, eyelids/eyelashes occlusions and overlapping intensities, which makes the segmentation process difficult. An efficient method has been proposed for the segmentation of iris images that deal with non-circular iris boundaries and other noise artefacts mentioned above. The proposed method uses the Otsu multilevel thresholding based on improved particle swarm optimisation technique as a pre-segmentation step. Pre-segmentation step delimits the iris region from the other parts of an eye image. The geodesic active contours incorporated with a novel stopping function is then used to segment non-circular iris boundaries. The recognition accuracy of the proposed method is verified using the standard databases, CASIA v3 Interval and UBIRISv1. Obtained results have been compared with existing methods and have an encouraging performance.</p>2018-04-24T00:00:00ZMorphology-based structure-preserving projection for spectral–spatial feature extraction and classification of hyperspectral datahttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1431
<p>Incorporation of spatial information besides rich spectral information of hyperspectral image significantly enhances data classification accuracy. A morphology-based feature extraction and classification framework is proposed here, which includes the local neighbourhood information in a spatial window for extension of training set. The proposed method is morphology-based structure-preserving projection (MSPP) and tries to preserve the data structure in spectral–spatial feature space. Moreover, MSPP increases the class discrimination ability by defining a similarity matrix constructed by extended spectral–spatial training samples. The experimental results show the superiority of MSPP compared to some state-of-the-art classification methods from the classification accuracy point of view.</p>2018-04-23T00:00:00ZDeriving scale normalisation factors for a GLoG detectorhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0493
<p>In computer vision, blob detection is used to obtain regions of interest that could signal the presence of objects or parts with application to object recognition and object tracking. One of the more common blob detectors is based on the Laplacian of Gaussian (LoG). However, most blob detectors developed in the past assume circular blobs, and these detectors do not perform as well with elliptical blobs, a more prevalent scenario in real images. A generalised LoG (GLoG) detector was proposed recently to deal specifically with elliptical blobs. To formulate the GLoG in a multi-scale framework, its response must be made scale invariant. Toward that end, necessary and sufficient conditions are presented here, with the normalisation factors derived for a scale-invariant GLoG detector. The factors are validated with a synthetic example and are further tested with two real-world images.</p>2018-04-20T00:00:00ZDepth extraction method with subpixel matching for light-coding-based depth camerahttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.0567
<p>Depth images extracted by light-coding-based depth cameras are widely used to reconstruct three-dimensional scenes in recent years. However, the retrieved depth accuracy greatly influences the reconstruction quality. Here, the authors present an appropriate depth extraction method based on subpixel matching to improve the depth accuracy. The proposed method utilises nearest neighbour interpolation to the projector's image plane to obtain depth values at subpixel accuracy, thereby better preserving the important depth information without changing any inner structure of the depth camera. Experimental results show that the proposed method improves the depth images both on image quality and depth accuracy.</p>2018-04-20T00:00:00ZDaubechies wavelet-based local feature descriptor for multimodal medical image registrationhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1305
<p>A new local feature descriptor recursive Daubechies pattern (RDbW) is developed by defining and encoding the Daubechies wavelet decomposed center–neighbour pixel relationship in the local texture. RDbW features are applied in spatial alignment (registration) of multimodal medical images using a Procrustes analysis (PA)-based affine transformation function and the registered images are further fused by employing a wavelet-based fusion method. A significant amount of experiments is conducted and the registration and fusion accuracy of the proposed feature descriptor is compared with the prominent existing methods such as local binary patterns (LBP), local tetra pattern (LTrP), local diagonal extrema pattern (LDEP), and local diagonal Laplacian pattern (LDLP). Experimental results show the present registration method improves the average registration accuracy by 38, 47, 71, and 76% in contrast to LDLP, LDEP, LTrP, and LBP, respectively. Further, the fusion results of the current approach exhibit an average improvement in entropy by 11%, standard deviation by 6% edge strength by 12%, sharpness by 23%, and average gradient by 16% when compared with all other feature descriptors used for registering the images. Concepts presented here can be used widely in analysing the combined information present in multimodal medical images.</p>2018-04-20T00:00:00ZCross-channel regularisation for joint demosaicking and intrinsic lens deblurringhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0743
<p>Here, the authors present an effective regularisation approach to colour image demosaicking. The authors’ work is inspired by the interesting observation that the cross-channel dependencies of high-frequency details of a colour image are supposed to correspond in all the main colour channels acquired by the sensor. Therefore, minimising their difference in the demosaicking process can significantly improve the quality of the reconstructed image. The authors also demonstrate that mosaicked image formation strictly depends on the intrinsic lens blur. Hence, in the authors’ solution to the image demosaicking as an inverse imaging problem, they take the lens blur characteristics into account. The proposed regularisation method is also based on the fact that sensor saturation significantly alters the distribution of pixel intensity and Gaussian noise. The authors develop an efficient solution to the problem via the alternating direction method of multipliers numerical solver. As a result of these steps, the proposed demosaicking approach significantly enhances the quality of reconstructed images. Experimental results and quantitative evaluations demonstrate that the proposed method outperforms the existing image demosaicking methods.</p>2018-04-20T00:00:00ZUnrestricted LR detection for biomedical applications using coarse-to-fine hierarchical approachhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0509
<p>In this study, the authors present a simple, reliable, fast, unrestricted-shape geometry, and accurate algorithm which runs in <i>O</i>(log<sub>2</sub> <i>n</i>) time to find the axis-parallel largest rectangle (LR) inside a given region of interest (ROI), where <i>n</i> is the image size in one dimension, which means that the proposed model can work in real time. The proposed approach is successful in detecting the LR of arbitrary orientation that is fully contained in the ROI as well. Also, the present algorithm can find the largest empty rectangle in a space containing a set of zero points, whether the axis-parallel rectangle or the oriented one. The strategy followed here is to accelerate LR detection process by searching the rectangle with the largest area inscribed in the ROI, by starting first with the lowest-resolution version of the original image for determining the LR four corners’ coordinates, then next searching the new LR corners’ scaled coordinates in the higher power resolutions in a multiple resolutions hierarchical model and therefore, a corresponding coarse-to-fine inference procedure recursively eliminates the search space of the LR four corners coordinates. For finding the largest oriented rectangle, the same hierarchical procedures are followed, but combined with rotation-angle resolution.</p>2018-04-19T00:00:00ZNon-parametric mixture model with TV spatial regularisation and its dual expectation maximisation algorithmhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1251
<p>An image segmentation method based on a non-parametric mixture model together with total variation (TV) regularisation is proposed. The authors use a kernel density estimator as a basic mixture model, which can better separate the non-central distributed data. To enforce its robustness, they integrate the well-known TV regularisation into the statistical method. They use the dual method to efficiently solve the TV-related energy and get a new dual expectation maximisation algorithm. Experiments on both synthetic images and real images show that the proposed algorithm can achieve good segmentation results. Compared with the parametric models and hidden Markov random field-based method, the proposed method can produce better result in some cases.</p>2018-04-19T00:00:00ZEfficient 3D mesh salient region detection using local homogeneity measurehttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0598
<p>Visual saliency is defined by the perceptual information that makes possible to detect specific areas which attract to guide the human visual attention. In this study, the authors present an efficient method for salient regions detection on three-dimensional (3D) meshes using weighted graphs representation. To do so, the authors propose a novel 3D surface descriptor based on a local homogeneity measure. Then, they define the similarity measure between vertices using normal deviation similarities, a two-dimensional projection height map, and the mean curvature. The saliency of a vertex is then evaluated as its degree measure based on the local patch descriptor and a height map. In addition, the authors introduce a custom version of hill climbing algorithm in order to segment the 3D mesh regions according to the saliency degree. Furthermore, they show the robustness of their proposed method through different experimental results. Finally, the authors present the stability and robustness of their method with respect to noise.</p>2018-04-19T00:00:00ZBackground subtraction using Gaussian–Bernoulli restricted Boltzmann machinehttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1055
<p>The background subtraction is an important technique in computer vision which segments moving objects into video sequences by comparing each new frame with a learned background model. In this work, the authors propose a novel background subtraction method based on Gaussian–Bernoulli restricted Boltzmann machines (GRBMs). The GRBM is different from the ordinary restricted Boltzmann machine (RBM) by using real numbers as inputs, resulting in a constrained mixture of Gaussians, which is one of the most widely used techniques to solve the background subtraction problem. The GRBM makes it easy to learn the variance of pixel values and takes the advantage of the generative model paradigm of the RBM. They present a simple technique to reconstruct the learned background model from a given input frame and to extract the foreground from the background using the variance learned for each pixel. Furthermore, they demonstrate the effectiveness of the proposed technique with extensive experimentation and quantitative evaluation on several commonly used public data sets for background subtraction.</p>2018-04-19T00:00:00ZStatistical geometric components of straight lines (SGCSL) feature extraction method for offline Arabic/Persian handwritten words recognitionhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0839
<p>In this study, the authors present a new feature extraction method for handwritten Arabic/Persian language word recognition. This feature is based on the angle, number, location, and size of straight lines which represents geometric and quantitative attributes of a word. At first, word image is broken into an <i>m</i> × <i>n</i> window and straight lines are extracted from each window. Then, the proposed features are taken from these lines and combined together. Finally, the features of the images are used for training and testing support vector machine classifier. The proposed method is tested on three datasets: IBN-SINA and IFN/ENIT for Arabic words and Iran-cities for Persian words recognition. Recognition accuracy of the proposed method is about 67.47, 86.22 and 80.78% for the Iran-cities, IBN-SINA and IFN/ENIT Arabic dataset, respectively, which is better than state-of-the-art methods.</p>2018-04-18T00:00:00ZReview of wavelet-based unsupervised texture segmentation, advantage of adaptive waveletshttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1005
<p>Wavelet-based segmentation approaches are widely used for texture segmentation purposes because of their ability to characterise different textures. In this study, the authors assess the influence of the chosen wavelet and propose to use the recently introduced empirical wavelets. We show that the adaptability of the empirical wavelet permits to reach better results than classic wavelets. To focus only on the textural information, they also propose to perform a cartoon + texture decomposition step before applying the segmentation algorithm. The proposed method is tested on six classic benchmarks, based on several popular texture images.</p>2018-04-18T00:00:00ZPlateau limit-based tri-histogram equalisation for image enhancementhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1088
<p>An adaptive plateau limit-based histogram equalisation algorithm is suggested to enhance digital images. Histogram of the image is clipped with a plateau limit to avoid over enhancement. The plateau limit is derived from the average of the mean and the median intensity values to offer the improved enhancement. Clipped histogram is subdivided into three parts, using histogram subdivision limit parameters that are calculated on the basis of the standard deviation of the image. Histogram of individual sub-image is equalised independently and then combined into a single enhanced image. Experimental results demonstrate that the proposed plateau limit-based tri-histogram equalisation algorithm enhances the image quality. Compared with the other traditional plateau and non-plateau limit-based histogram equalisation algorithms, quantitative and visual quality assessments effectively validate the superiority of the proposed algorithm.</p>2018-04-18T00:00:00ZPlane detection in 3D point cloud using octree-balanced density down-sampling and iterative adaptive plane extractionhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1076
<p>In this paper, a new technique for plane detection from 3D point clouds is proposed. The algorithm depends on two concepts to balance between high-accuracy and fast performance. The first is the use of a new fast octree-based balanced density down-sampling technique to reduce the number of points. The second is the fact that the number of planes in any dataset is much less than the number of the points. Random points are examined to find the 3D planes. To increase the accuracy, the system utilizes an adaptive plane extraction technique to overcome data noise. Initially, the point cloud is subdivided using octree into small cubes with a limited number of points. Then the cubes are down-sampled based on the local density of each cube. The points are explored randomly for finding a planar surface by applying principal component analysis (PCA) on the points’ spherical neighborhood obtained by the down-sampled octree structure. The adaptive plane extraction is used to adjust the plane orientation to find the best position that includes the maximum number of points. Experimental results demonstrate that the proposed algorithm is capable of processing large amounts of data efficiently to produce accurate results that are robust to noise.</p>2018-04-18T00:00:00ZRegularisation learning of correlation filters for robust visual trackinghttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1043
<p>Recently, kernelised correlation filter (KCF)-based trackers aroused increasing interest and achieved extremely compelling results in different competitions and benchmarks in the field of visual object tracking. However, the training mechanism of the KCF that exploits simple linear combinations of filter from the previous frame easily cause error accumulation. To overcome this problem, the authors propose a novel training strategy that utilises all of the previous training samples, and a sparsity-related loss function regularised by the <i>L</i>1 norm to deal with the problem of the fixed template size in KCF trackers, a separate scale filter is learned for scale estimation during the tracking process. Moreover, powerful features that include histogram of oriented gradients (HOG) and colour features are integrated to further improve the robustness of the authors’ tracking. Extensive experiments in various challenging situations demonstrate that the proposed method performs favourably against several state-of-the-art tracking algorithms.</p>2018-04-17T00:00:00ZSurvey on various lane and driver detection techniques based on image processing for hilly terrainhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0864
<p>As vehicular traffic continues to grow traffic management and prevention of accidents has become a major concern. This problem only gets magnified when travel on the mountainous roads are considered. This study is especially focused toward Himalayan mountains as they pose a greater risk because of their rugged natural setting. This study investigates crucial problems faced on the hilly roads and the challenges in translating existing driver-assistance systems to such roads. The survey probes every lane detection algorithms, image processing techniques and various assistance features for applicability to hilly roads discussing the pros and cons for each of them. Conclusions are drawn as to deduce the more suitable methods that can be improvised and re-tuned to adapt them for mountainous roads.</p>2018-04-10T00:00:00ZStructure-based interpolation method for restoring the intensity of low-density impulse noisehttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0948
<p>Restoring pixel intensities corrupted by impulse noise has a great impact on the quality of decision-based filters. In this study, the authors’ focus is on intensity restoration of noisy pixels. Their assumption is that noisy pixels are already established by the noise-detection unit being considered as missing data in the image. When the interpolation methods are adopted in the noise-restoration unit of the decision-based filters for the purpose of restoring the intensities of the noisy pixels, two unexpected problems emerge – jagged edges and blurred details. These drawbacks can be ameliorated by using extra information obtained from structures in the images. Their structure-based interpolation method comprises two steps: pre-interpolation and post-interpolation. In the first step (pre-interpolation), the Sibson natural neighbour interpolation is adopted for the initial estimation of the intensities of all noisy pixels. In the second step (post-interpolation, modifying-phase), for each noisy pixel in pre-interpolated image, the intensity variations of the pixels on two adjacent parallel lines, in different directions in their corresponding local windows, are analysed. Based on the obtained structural information, the intensity of the centred noisy pixel is restored more effectively. Since the structures in the images are far more noticeable at low-density impulse noise, the proposed method works more efficiently in this case; however, a gradual improvement is achieved for high-density impulse noise.</p>2018-04-10T00:00:00ZImproved visual/infrared colour fusion method with double-opponency colour constancy mechanismhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0276
<p>To solve problems that colour distortion and low resolution of infrared and visible colour fusion images, the authors propose a fusion method based on the double-opponency colour constancy mechanism of human vision. First, Waxman's fusion method which imitated the neuro-dynamics mechanism of the rattlesnake bimodal cell is used to fuse the visible light with the source multi-band images to generate pseudo-colour images. Second, a double-opponent colour constancy computation model based on Rodieck's double difference-of-Gaussian is proposed to obtain the estimate of an illuminant of colour fusion images. Finally, the colour fusion images are corrected by the diagonal transformation model based on the cone adaptive mechanism. They also propose to use the three-dimensional RGB histogram to analyse the colour distribution of colour fusion images. In the comparison experiments with other approaches using the three-dimensional RGB histogram, one can see that the proposed fusion method gives colour image coinciding well with natural colour distribution and satisfies human perception needs.</p>2018-04-10T00:00:00ZEfficient direction-oriented search algorithm for block motion estimationhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0641
<p>Motion estimation is one of the most crucial and time-consuming component of video compression methods. However, much research has been done to improve computational complexity at the expense of the loss in performance of matching of blocks. A novel block matching algorithm named efficient direction-oriented search is proposed. For this, the proposed algorithm firstly aims to dynamically switch between search regions based on the location of minimum distortion error. The search region dimension is also made adaptive for faster convergence. Then the computational complexity is reduced by using a proposed horizontal, vertical wings diamond search pattern and, two <script type="math/mml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>±</mml:mo> <mml:msup> <mml:mn>45</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> </script> inclined hexagon-shaped direction-oriented search patterns. For further speed-up in the search process, partial distortion calculations are employed. A method for optimal threshold value selection based on the distortion statistics for different partial distortion calculations is presented. The performance of the proposed algorithm is evaluated for different video sequences containing: slow, medium, fast, and directional motion content. The experimental results indicate that significant improvement in speed-up can be achieved while maintaining the better peak signal-to-noise-ratio performance. For directional motion video sequences, the proposed method even outperforms the full search algorithm with a significantly lower computational cost.</p>2018-04-10T00:00:00ZConvolutional neural network in network (CNNiN): hyperspectral image classification and dimensionality reductionhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1375
<p>Classification is a principle technique in hyperspectral images (HSIs), where a label is assigned to each pixel based on its characteristics. However, due to lack of labelled training instances in HSIs and also its ultra-high dimensionality, deep learning approaches need a special consideration for HSI classification. As one of the first works in the HSI classification, this study proposes a novel network pipeline called convolutional neural network in network (which is deeper than the existing approaches) by jointly utilising the spatial and spectral information and produces high-level features from the original HSI. This can occur by using spatial–spectral relationships of individual pixel vector at the initial component of the proposed pipeline; the extracted features are then combined to form a joint spatial–spectral feature map. Finally, a recurrent neural network is trained on the extracted features which contain wealthy spectral and spatial properties of the HSI to predict the corresponding label of each vector. The model has been tested on two large scale hyperspectral datasets in terms of classification accuracy, training error, and computational time.</p>2018-04-10T00:00:00ZStudy on the method of colour image noise reduction based on optimal channel-processinghttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0871
<p>The methods of the image noise reduction based on optimal channel-processing to enhance image quality are studied. According to different noise with the different ratio in colour components, different noise reduction technologies are used for noise reduction. Then the optimal algorithm is automatically selected as the ultimate way of noise reduction in each channel. For the purpose of optimal noise reduction effect, this study presents a method of combining the quadratic optimisation with the variable window processing. The quadratic optimisation provides a good environment for noise reduction by decreasing complexity of mixed noise and the variable window processing calibrates the image smoothing result. Compared with mean filtering, median filtering and adaptive filtering, the image quality processed by the proposed algorithm is generally improved by >2 dB.</p>2018-04-09T00:00:00ZEfficient approach for the automatic detection of haemorrhages in colour retinal imageshttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1036
<p>Advances in the eye care telemedicine system aid the diabetic patients in remote areas to stop the unwanted visit to ophthalmologist, reduces overall cost, time and money. Diabetic retinopathy, which is the primary cause of sight loss, has the most common symptoms like microaneurysms, haemorrhages, cotton-wool spots, exudates and drusen. In this work, an efficient approach for the automatic detection of haemorrhages in colour retinal images is proposed and validated. The colour retinal images captured from the diabetic patients are enhanced by an effective pre-processor. A bag of features based on intensity, colour and texture are extracted. Finally, the features are classified with the help of partial least square classifier. The classifier performance is validated on two publicly available databanks. The developed method obtains an area under receiver operating characteristic curve of 0.98 with an average execution time of 6 s. This application outperforms the existing approaches with high robustness and efficiency.</p>2018-04-09T00:00:00ZBackprojection inverse filtration for laminographic reconstructionhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1344
<p>Traditional tomography uses circular trajectories and here, filtered backprojection often works well. However, for objects with large aspect ratios, rotational tomography is often not feasible. In these cases, other trajectories can be more appropriate. For generic trajectories, filtered backprojection methods might not work well and full iterative reconstruction can be computationally demanding. In this study, the authors thus propose a third paradigm that combines aspects of both of these techniques. They use interpolation and backprojection techniques to generate an initial estimate of an object's internal structure using projection images taken at different orientations. Depending on the scanning geometry used to calculate the tomographic projections, this initial estimate can be understood as a blurred (filtered) approximation of the actual structure. For each scanning geometry, they specify the equivalent blurring operator that would provide the same estimate directly from a representation of the object's internal structure. They then use iterative techniques to invert this filtering operation, thus estimating the internal structure from the estimate of its blurred representation.</p>2018-04-09T00:00:00ZVolume 12, Issue 4http://digital-library.theiet.org/content/journals/iet-ipr/12/4
2018-04-01T00:00:00ZImage smoothing via a scale-aware filter and L0 normhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0719
<p>It is difficult to preserve diminishing weak structures and edges, and remove complex details simultaneously in the context of image smoothing. While most of existing methods only take either local or global features into consideration, the authors propose two methods taking advantage of both to achieve smoothing, both of which consist of two steps and share the same first step. In the first step, the authors use a scale-aware approach to generate a guidance image by blurring the small-scale components in the input image. Such approach, based on the rolling guidance framework with domain transform filter and bilateral filter, can prevent diminishing the corners of the main structures. Subsequently, the authors use the two proposed methods, with the guidance image as input, to remove blurry details. The first method introduces two data fidelity terms into <i>L</i> <sub>0</sub> gradient minimisation and removes high-contrast details, which is a structure-preserving method. The other method, an edge-preserving method, uses an adaptive <i>L</i> <sub>0</sub> gradient minimisation technique, facilitating the preservation of the weak structures and edges. The smoothing factors in such technique are decide by the corresponding gradient of each pixel of the guidance image. The authors apply both methods to various image processing fields.</p>2018-03-28T00:00:00ZAnti-occlusion particle filter object-tracking method based on feature fusionhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1068
<p>A new anti-occlusion particle filter object-tracking method based on feature fusion is proposed in this study. Colour and local binary pattern features are extracted and additively fused with a deterministic coefficient, which is calculated based on the difference between the object features and the background. An integral cumulative histogram is proposed to reduce the computational cost of feature extraction. A new occlusion determination method is proposed, and corresponding tracking strategies are also put forward for various occlusion conditions; in the case of partial occlusion, block tracking is carried out, and in the case of serious occlusion, the least-square method is used to predict the object position. Context Aware Vision using Image-based Active Recognition (CAVIAR) and Video Image Retrieval and Analysis Tool (VIRAT) video libraries are used to validate the method. The experimental results show that the proposed method can describe an object effectively and improve tracking stability and robustness under the occlusion conditions.</p>2018-03-28T00:00:00ZVolume 12, Issue 3http://digital-library.theiet.org/content/journals/iet-ipr/12/3
2018-03-01T00:00:00ZTarget detection of hyperspectral image based on spectral saliencyhttp://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1173
<p>Target detection of hyperspectral image (HSI) is a research hotspot in the field of remote sensing. It is of particular importance in many domains, especially in military application. Unsupervised target detection is usually more difficult because there is no prior information about target. Traditional algorithms exploit spectral information, only. This study introduces the idea of saliency detection from the visual technique into HSI processing domain and proposes a novel approach named spectral saliency target detection (SSD). It establishes a novel salient model, which utilises both spatial saliency and spectral saliency. In the framework of SSD, it combines the model with spectral matching algorithm to make it perform well even in situations where the target is concealed and small. A HSI set comprised of eight different scenes with complex background is setup to evaluate the performance of the proposed algorithm. The final visible detection results demonstrate that the SSD algorithm outperforms the others. The receiver operation characteristic (ROC) curve and area under the ROC curve are applied to evaluate the results. The proposed algorithm shows superior and stable performance.</p>2018-02-19T00:00:00ZVolume 12, Issue 2http://digital-library.theiet.org/content/journals/iet-ipr/12/2
2018-02-01T00:00:00ZVolume 12, Issue 1http://digital-library.theiet.org/content/journals/iet-ipr/12/1
2018-01-01T00:00:00ZVolume 11, Issue 12http://digital-library.theiet.org/content/journals/iet-ipr/11/12
2017-12-01T00:00:00ZVolume 11, Issue 11http://digital-library.theiet.org/content/journals/iet-ipr/11/11
2017-11-01T00:00:00ZVolume 11, Issue 10http://digital-library.theiet.org/content/journals/iet-ipr/11/10
2017-10-01T00:00:00ZVolume 11, Issue 9http://digital-library.theiet.org/content/journals/iet-ipr/11/9
2017-09-01T00:00:00ZVolume 11, Issue 8http://digital-library.theiet.org/content/journals/iet-ipr/11/8
2017-08-01T00:00:00ZVolume 11, Issue 7http://digital-library.theiet.org/content/journals/iet-ipr/11/7
2017-07-01T00:00:00ZVolume 11, Issue 6http://digital-library.theiet.org/content/journals/iet-ipr/11/6
2017-06-01T00:00:00ZVolume 11, Issue 5http://digital-library.theiet.org/content/journals/iet-ipr/11/5
2017-04-01T00:00:00ZVolume 11, Issue 4http://digital-library.theiet.org/content/journals/iet-ipr/11/4
2017-04-01T00:00:00ZVolume 11, Issue 3http://digital-library.theiet.org/content/journals/iet-ipr/11/3
2017-03-01T00:00:00ZVolume 11, Issue 2http://digital-library.theiet.org/content/journals/iet-ipr/11/2
2017-02-01T00:00:00ZVolume 11, Issue 1http://digital-library.theiet.org/content/journals/iet-ipr/11/1
2017-01-01T00:00:00ZVolume , Issuehttp://digital-library.theiet.org/content/journals/iet-ipr//
2017-01-01T00:00:00ZVolume 10, Issue 12http://digital-library.theiet.org/content/journals/iet-ipr/10/12
2016-12-01T00:00:00ZVolume 10, Issue 11http://digital-library.theiet.org/content/journals/iet-ipr/10/11
2016-11-01T00:00:00ZVolume 10, Issue 10http://digital-library.theiet.org/content/journals/iet-ipr/10/10
2016-10-01T00:00:00ZVolume 10, Issue 9http://digital-library.theiet.org/content/journals/iet-ipr/10/9
2016-09-01T00:00:00ZVolume 10, Issue 8http://digital-library.theiet.org/content/journals/iet-ipr/10/8
2016-08-01T00:00:00ZVolume 10, Issue 7http://digital-library.theiet.org/content/journals/iet-ipr/10/7
2016-07-01T00:00:00ZVolume 10, Issue 6http://digital-library.theiet.org/content/journals/iet-ipr/10/6
2016-06-01T00:00:00ZVolume 10, Issue 5http://digital-library.theiet.org/content/journals/iet-ipr/10/5
2016-05-01T00:00:00ZVolume 10, Issue 4http://digital-library.theiet.org/content/journals/iet-ipr/10/4
2016-04-01T00:00:00ZVolume 10, Issue 3http://digital-library.theiet.org/content/journals/iet-ipr/10/3
2016-03-01T00:00:00ZVolume 10, Issue 2http://digital-library.theiet.org/content/journals/iet-ipr/10/2
2016-02-01T00:00:00ZVolume 10, Issue 1http://digital-library.theiet.org/content/journals/iet-ipr/10/1
2016-01-01T00:00:00ZVolume 9, Issue 12http://digital-library.theiet.org/content/journals/iet-ipr/9/12
2015-12-01T00:00:00ZVolume 9, Issue 11http://digital-library.theiet.org/content/journals/iet-ipr/9/11
2015-11-01T00:00:00ZVolume 9, Issue 10http://digital-library.theiet.org/content/journals/iet-ipr/9/10
2015-10-01T00:00:00ZVolume 9, Issue 9http://digital-library.theiet.org/content/journals/iet-ipr/9/9
2015-09-01T00:00:00ZVolume 9, Issue 8http://digital-library.theiet.org/content/journals/iet-ipr/9/8
2015-08-01T00:00:00ZVolume 9, Issue 7http://digital-library.theiet.org/content/journals/iet-ipr/9/7
2015-07-01T00:00:00ZVolume 9, Issue 6http://digital-library.theiet.org/content/journals/iet-ipr/9/6
2015-06-01T00:00:00ZVolume 9, Issue 5http://digital-library.theiet.org/content/journals/iet-ipr/9/5
2015-05-01T00:00:00ZVolume 9, Issue 4http://digital-library.theiet.org/content/journals/iet-ipr/9/4
2015-04-01T00:00:00ZVolume 9, Issue 3http://digital-library.theiet.org/content/journals/iet-ipr/9/3
2015-03-01T00:00:00ZVolume 9, Issue 2http://digital-library.theiet.org/content/journals/iet-ipr/9/2
2015-02-01T00:00:00ZVolume 9, Issue 1http://digital-library.theiet.org/content/journals/iet-ipr/9/1
2015-01-01T00:00:00ZVolume 8, Issue 12http://digital-library.theiet.org/content/journals/iet-ipr/8/12
2014-12-01T00:00:00ZVolume 8, Issue 11http://digital-library.theiet.org/content/journals/iet-ipr/8/11
2014-11-01T00:00:00ZVolume 8, Issue 10http://digital-library.theiet.org/content/journals/iet-ipr/8/10
2014-10-01T00:00:00ZVolume 8, Issue 9http://digital-library.theiet.org/content/journals/iet-ipr/8/9
2014-09-01T00:00:00ZVolume 8, Issue 8http://digital-library.theiet.org/content/journals/iet-ipr/8/8
2014-08-01T00:00:00ZVolume 8, Issue 7http://digital-library.theiet.org/content/journals/iet-ipr/8/7
2014-07-01T00:00:00ZVolume 8, Issue 6http://digital-library.theiet.org/content/journals/iet-ipr/8/6
2014-06-01T00:00:00ZVolume 8, Issue 5http://digital-library.theiet.org/content/journals/iet-ipr/8/5
2014-05-01T00:00:00ZVolume 8, Issue 4http://digital-library.theiet.org/content/journals/iet-ipr/8/4
2014-04-01T00:00:00ZVolume 8, Issue 3http://digital-library.theiet.org/content/journals/iet-ipr/8/3
2014-03-01T00:00:00ZVolume 8, Issue 2http://digital-library.theiet.org/content/journals/iet-ipr/8/2
2014-02-01T00:00:00ZVolume 8, Issue 1http://digital-library.theiet.org/content/journals/iet-ipr/8/1
2014-01-01T00:00:00ZVolume 7, Issue 9http://digital-library.theiet.org/content/journals/iet-ipr/7/9
2013-12-01T00:00:00ZVolume 7, Issue 8http://digital-library.theiet.org/content/journals/iet-ipr/7/8
2013-11-01T00:00:00ZVolume 7, Issue 7http://digital-library.theiet.org/content/journals/iet-ipr/7/7
2013-10-01T00:00:00ZVolume 7, Issue 6http://digital-library.theiet.org/content/journals/iet-ipr/7/6
2013-08-01T00:00:00ZVolume 7, Issue 5http://digital-library.theiet.org/content/journals/iet-ipr/7/5
2013-07-01T00:00:00ZVolume 7, Issue 4http://digital-library.theiet.org/content/journals/iet-ipr/7/4
2013-06-01T00:00:00ZVolume 7, Issue 3http://digital-library.theiet.org/content/journals/iet-ipr/7/3
2013-04-01T00:00:00ZVolume 7, Issue 2http://digital-library.theiet.org/content/journals/iet-ipr/7/2
2013-03-01T00:00:00ZVolume 7, Issue 1http://digital-library.theiet.org/content/journals/iet-ipr/7/1
2013-02-01T00:00:00ZVolume 6, Issue 9http://digital-library.theiet.org/content/journals/iet-ipr/6/9
2012-12-01T00:00:00ZVolume 6, Issue 8http://digital-library.theiet.org/content/journals/iet-ipr/6/8
2012-11-01T00:00:00ZVolume 6, Issue 7http://digital-library.theiet.org/content/journals/iet-ipr/6/7
2012-10-01T00:00:00ZVolume 6, Issue 6http://digital-library.theiet.org/content/journals/iet-ipr/6/6
2012-08-01T00:00:00ZVolume 6, Issue 5http://digital-library.theiet.org/content/journals/iet-ipr/6/5
2012-07-01T00:00:00ZVolume 6, Issue 4http://digital-library.theiet.org/content/journals/iet-ipr/6/4
2012-06-01T00:00:00ZVolume 6, Issue 3http://digital-library.theiet.org/content/journals/iet-ipr/6/3
2012-04-01T00:00:00ZVolume 6, Issue 2http://digital-library.theiet.org/content/journals/iet-ipr/6/2
2012-03-01T00:00:00ZVolume 6, Issue 1http://digital-library.theiet.org/content/journals/iet-ipr/6/1
2012-02-01T00:00:00ZVolume 5, Issue 8http://digital-library.theiet.org/content/journals/iet-ipr/5/8
2011-12-01T00:00:00ZVolume 5, Issue 7http://digital-library.theiet.org/content/journals/iet-ipr/5/7
2011-10-01T00:00:00ZVolume 5, Issue 6http://digital-library.theiet.org/content/journals/iet-ipr/5/6
2011-09-01T00:00:00ZVolume 5, Issue 5http://digital-library.theiet.org/content/journals/iet-ipr/5/5
2011-08-01T00:00:00ZVolume 5, Issue 4http://digital-library.theiet.org/content/journals/iet-ipr/5/4
2011-06-01T00:00:00ZVolume 5, Issue 3http://digital-library.theiet.org/content/journals/iet-ipr/5/3
2011-04-01T00:00:00ZVolume 5, Issue 2http://digital-library.theiet.org/content/journals/iet-ipr/5/2
2011-03-01T00:00:00ZVolume 5, Issue 1http://digital-library.theiet.org/content/journals/iet-ipr/5/1
2011-02-01T00:00:00ZVolume 4, Issue 6http://digital-library.theiet.org/content/journals/iet-ipr/4/6
2010-12-01T00:00:00ZVolume 4, Issue 5http://digital-library.theiet.org/content/journals/iet-ipr/4/5
2010-10-01T00:00:00ZVolume 4, Issue 4http://digital-library.theiet.org/content/journals/iet-ipr/4/4
2010-08-01T00:00:00ZVolume 4, Issue 3http://digital-library.theiet.org/content/journals/iet-ipr/4/3
2010-06-01T00:00:00ZVolume 4, Issue 2http://digital-library.theiet.org/content/journals/iet-ipr/4/2
2010-04-01T00:00:00ZVolume 4, Issue 1http://digital-library.theiet.org/content/journals/iet-ipr/4/1
2010-02-01T00:00:00ZVolume 3, Issue 6http://digital-library.theiet.org/content/journals/iet-ipr/3/6
2009-12-01T00:00:00ZVolume 3, Issue 5http://digital-library.theiet.org/content/journals/iet-ipr/3/5
2009-10-01T00:00:00ZVolume 3, Issue 4http://digital-library.theiet.org/content/journals/iet-ipr/3/4
2009-08-01T00:00:00ZVolume 3, Issue 3http://digital-library.theiet.org/content/journals/iet-ipr/3/3
2009-06-01T00:00:00ZVolume 3, Issue 2http://digital-library.theiet.org/content/journals/iet-ipr/3/2
2009-04-01T00:00:00ZVolume 3, Issue 1http://digital-library.theiet.org/content/journals/iet-ipr/3/1
2009-02-01T00:00:00ZVolume 2, Issue 6http://digital-library.theiet.org/content/journals/iet-ipr/2/6
2008-12-01T00:00:00ZVolume 2, Issue 5http://digital-library.theiet.org/content/journals/iet-ipr/2/5
2008-10-01T00:00:00ZVolume 2, Issue 4http://digital-library.theiet.org/content/journals/iet-ipr/2/4
2008-08-01T00:00:00ZVolume 2, Issue 3http://digital-library.theiet.org/content/journals/iet-ipr/2/3
2008-06-01T00:00:00ZVolume 2, Issue 2http://digital-library.theiet.org/content/journals/iet-ipr/2/2
2008-04-01T00:00:00ZVolume 2, Issue 1http://digital-library.theiet.org/content/journals/iet-ipr/2/1
2008-02-01T00:00:00ZVolume 1, Issue 4http://digital-library.theiet.org/content/journals/iet-ipr/1/4
2007-12-01T00:00:00ZVolume 1, Issue 3http://digital-library.theiet.org/content/journals/iet-ipr/1/3
2007-09-01T00:00:00ZVolume 1, Issue 2http://digital-library.theiet.org/content/journals/iet-ipr/1/2
2007-06-01T00:00:00ZVolume 1, Issue 1http://digital-library.theiet.org/content/journals/iet-ipr/1/1
2007-03-01T00:00:00Z